{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive Plots and Diagrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "\t} catch (e) {\n",
       "\t    $([IPython.events]).on('kernel_created.Kernel kernel_created.Session', initComm);\n",
       "\t}\n",
       "    });\n",
       "})(IPython, jQuery, _, MathJax);\n",
       "</script>\n",
       "    <script>\n",
       "        window.interactLoadedFlag = true\n",
       "       $(\"#interact-js-shim\").bind(\"destroyed\", function () {\n",
       "           if (window.interactLoadedFlag) {\n",
       "               console.warn(\"JavaScript required by Interact will be removed if you remove this cell or run using Interact more than once.\")\n",
       "           }\n",
       "       })\n",
       "       $([IPython.events]).on(\"kernel_starting.Kernel kernel_restarting.Kernel\", function () { window.interactLoadedFlag = false })\n",
       "   </script>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Reactive, Interact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interactive plotting can be useful and fun. Here we have a few examples to get you started creating your own interactive plots. We will extensively use the `@manipulate` macro from the [introductory notebook](Introduction.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Compose](http://composejl.org) is an excellent tool for creating declarative vector graphics. Here is an example compose diagram you can play around with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
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       "return a};v.toString=function(){return\"Snap\\u2018s set\"};v.type=\"set\";a.set=function(){var a=new z;arguments.length&&a.push.apply(a,Array.prototype.slice.call(arguments,0));return a}});C.plugin(function(a,v,y,C){function A(a){var b=a[0];switch(b.toLowerCase()){case \"t\":return[b,0,0];case \"m\":return[b,1,0,0,1,0,0];case \"r\":return 4==a.length?[b,0,a[2],a[3] ]:[b,0];case \"s\":return 5==a.length?[b,1,1,a[3],a[4] ]:3==a.length?[b,1,1]:[b,1]}}function w(b,d,f){d=q(d).replace(/\\.{3}|\\u2026/g,b);b=a.parseTransformString(b)||\n",
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       "to:u(v),f:n(p)}}function z(a){return a}function d(a){return function(b){return+b.toFixed(3)+a}}function f(b){return a.rgb(b[0],b[1],b[2])}function n(a){var b=0,d,f,k,n,h,p,q=[];d=0;for(f=a.length;d<f;d++){h=\"[\";p=['\"'+a[d][0]+'\"'];k=1;for(n=a[d].length;k<n;k++)p[k]=\"val[\"+b++ +\"]\";h+=p+\"]\";q[d]=h}return Function(\"val\",\"return Snap.path.toString.call([\"+q+\"])\")}function u(a){for(var b=[],d=0,f=a.length;d<f;d++)for(var k=1,n=a[d].length;k<n;k++)b.push(a[d][k]);return b}var p={},b=/[a-z]+$/i,q=String;\n",
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       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
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       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}},Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}}}((0.0w,0.0h),(1.0w,1.0h)),Nullable{Compose.UnitBox{S,T,U,V}}(),Nullable{Compose.Rotation{P<:Tuple{Vararg{Measures.Measure,N}}}}(),Nullable{Compose.Mirror}(),List([]),List([Compose.Form{Compose.SimplePolygonPrimitive{Tuple{Measures.Measure,Measures.Measure}}}(Compose.SimplePolygonPrimitive{Tuple{Measures.Measure,Measures.Measure}}[Compose.SimplePolygonPrimitive{Tuple{Measures.Measure,Measures.Measure}}(Tuple{Measures.Measure,Measures.Measure}[(0.495796cx,1.76708e-5cy),(0.246368cx,0.0691044cy),(0.0649008cx,0.253649cy),(1.76708e-5cx,0.504204cy),(0.0691044cx,0.753632cy),(0.253649cx,0.935099cy),(0.504204cx,0.999982cy),(0.753632cx,0.930896cy),(0.935099cx,0.746351cy),(0.999982cx,0.495796cy),(0.930896cx,0.246368cy),(0.746351cx,0.0649008cy),(0.495796cx,1.76708e-5cy)])],Symbol(\"\"))]),List([Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[Compose.FillPrimitive(RGBA{Float64}(1.0,1.0,0.0,1.0))])]),0,false,false,false,false,nothing,nothing,0.0,Symbol(\"\"))"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "comm_id": "df9b9780-37a9-4503-872d-2a3a0acff8e0",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Colors\n",
    "using Compose\n",
    "\n",
    "@manipulate for color=[\"yellow\", \"cyan\", \"tomato\"], rotate=0:.05:2π, n=3:20\n",
    "    compose(context(), fill(parse(Colorant, color)),\n",
    "    polygon([((1+sin(θ+rotate))/2, (1+cos(θ+rotate))/2) for θ in 0:2π/n:2π]))\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gadfly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "using Gadfly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/html": [],
      "text/plain": [
       "Interact.Options{:ToggleButtons,Function}(Signal{Function}(sin, nactions=1),\"f\",sin,\"sin\",Interact.OptionDict(DataStructures.OrderedDict{String,Function}(\"sin\"=>sin,\"cos\"=>cos),Dict{Function,String}(Pair{Function,String}(sin,\"sin\"),Pair{Function,String}(cos,\"cos\"))),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Checkbox(Signal{Bool}(false, nactions=0),\"both\",false)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
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       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-7145cb9e\");\n",
       "fig.select(\"#img-7145cb9e-5\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-7145cb9e-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-12\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-12\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-12\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-14\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-14\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-14\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-7145cb9e-15\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-15\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-15\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-15\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-15\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-16\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-16\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-7145cb9e-16\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "comm_id": "a02bddbb-b31e-4aae-a232-8f06d390a3a4",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@manipulate for ϕ = 0:π/16:4π, f = [sin, cos], both = false\n",
    "    if both\n",
    "        plot([θ -> sin(θ + ϕ), θ -> cos(θ + ϕ)], 0, 8)\n",
    "    else\n",
    "        plot(θ -> f(θ + ϕ), 0, 8)\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Int64}(Signal{Int64}(13, nactions=1),\"n\",13,\"13\",Interact.OptionDict(DataStructures.OrderedDict(\"1\"=>1,\"2\"=>2,\"3\"=>3,\"4\"=>4,\"5\"=>5,\"6\"=>6,\"7\"=>7,\"8\"=>8,\"9\"=>9,\"10\"=>10…),Dict(18=>\"18\",2=>\"2\",16=>\"16\",11=>\"11\",21=>\"21\",7=>\"7\",9=>\"9\",25=>\"25\",10=>\"10\",19=>\"19\"…)),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:ToggleButtons,DataType}(Signal{DataType}(Gadfly.Geom.PointGeometry, nactions=1),\"g\",Gadfly.Geom.PointGeometry,\"Gadfly.Geom.PointGeometry\",Interact.OptionDict(DataStructures.OrderedDict(\"Gadfly.Geom.PointGeometry\"=>Gadfly.Geom.PointGeometry,\"Gadfly.Geom.LineGeometry\"=>Gadfly.Geom.LineGeometry),Dict(Gadfly.Geom.PointGeometry=>\"Gadfly.Geom.PointGeometry\",Gadfly.Geom.LineGeometry=>\"Gadfly.Geom.LineGeometry\")),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
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       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#img-4a958512\");\n",
       "fig.select(\"#img-4a958512-5\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#img-4a958512-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-14\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-14\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-14\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#img-4a958512-16\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#img-4a958512-16\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#img-4a958512-16\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#img-4a958512-17\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#img-4a958512-17\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#img-4a958512-17\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-17\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-17\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#img-4a958512-18\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-18\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#img-4a958512-18\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "comm_id": "406c2260-5748-49a5-b4b4-cd1fc6c9f0f7",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@manipulate for n=1:25, g=[Geom.point, Geom.line]\n",
    "    Gadfly.plot(y=rand(n), x=rand(n), g)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mINFO: Recompiling stale cache file /Users/job/.julia/lib/v0.5/PyPlot.ji for module PyPlot.\n",
      "\u001b[0mWARNING: using PyPlot.plot in module Main conflicts with an existing identifier.\n"
     ]
    }
   ],
   "source": [
    "using PyPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since PyPlot images are often displayed as the result of function side-effects, you'll need to take an extra step in order for interactive PyPlot graphics to be updated properly as widget values are updated. You do this by using the `withfig` function to specify a figure object that will be updated in each iteration of `@manipulate`. Notice `f = figure()` and `withfig(f)` in the example below. The rest of it is straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Float64}(Signal{Float64}(2.0, nactions=1),\"α\",2.0,\"2.0\",Interact.OptionDict(DataStructures.OrderedDict(\"1.0\"=>1.0,\"1.1\"=>1.1,\"1.2\"=>1.2,\"1.3\"=>1.3,\"1.4\"=>1.4,\"1.5\"=>1.5,\"1.6\"=>1.6,\"1.7\"=>1.7,\"1.8\"=>1.8,\"1.9\"=>1.9…),Dict(1.2=>\"1.2\",2.8=>\"2.8\",2.0=>\"2.0\",1.5=>\"1.5\",2.2=>\"2.2\",2.1=>\"2.1\",1.4=>\"1.4\",2.6=>\"2.6\",2.3=>\"2.3\",2.9=>\"2.9\"…)),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Float64}(Signal{Float64}(2.0, nactions=1),\"β\",2.0,\"2.0\",Interact.OptionDict(DataStructures.OrderedDict(\"1.0\"=>1.0,\"1.1\"=>1.1,\"1.2\"=>1.2,\"1.3\"=>1.3,\"1.4\"=>1.4,\"1.5\"=>1.5,\"1.6\"=>1.6,\"1.7\"=>1.7,\"1.8\"=>1.8,\"1.9\"=>1.9…),Dict(1.2=>\"1.2\",2.8=>\"2.8\",2.0=>\"2.0\",1.5=>\"1.5\",2.2=>\"2.2\",2.1=>\"2.1\",1.4=>\"1.4\",2.6=>\"2.6\",2.3=>\"2.3\",2.9=>\"2.9\"…)),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Float64}(Signal{Float64}(2.0, nactions=1),\"γ\",2.0,\"2.0\",Interact.OptionDict(DataStructures.OrderedDict(\"1.0\"=>1.0,\"1.1\"=>1.1,\"1.2\"=>1.2,\"1.3\"=>1.3,\"1.4\"=>1.4,\"1.5\"=>1.5,\"1.6\"=>1.6,\"1.7\"=>1.7,\"1.8\"=>1.8,\"1.9\"=>1.9…),Dict(1.2=>\"1.2\",2.8=>\"2.8\",2.0=>\"2.0\",1.5=>\"1.5\",2.2=>\"2.2\",2.1=>\"2.1\",1.4=>\"1.4\",2.6=>\"2.6\",2.3=>\"2.3\",2.9=>\"2.9\"…)),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Textbox{AbstractString}(Signal{AbstractString}(a funny plot, nactions=1),\"leg\",nothing,\"a funny plot\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x323676790>)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "comm_id": "58c27f8e-480c-4456-a2a6-a19d4a9e0e62",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = figure()\n",
    "x = linspace(0,2π,1000)\n",
    "@manipulate for α=1:0.1:3, β=1:0.1:3, γ=1:0.1:3, leg=\"a funny plot\"; withfig(f) do\n",
    "        PyPlot.plot(x, cos(α*x + sin(β*x + γ)))\n",
    "        legend([leg])\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an added bonus, you can even fire up a Python GUI with `pygui(true)` and be able to use the widgets above to update the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# PyPlot Subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manipulating a PyPlot figure with multiple subplots adds an extra layer of complication. The `withfig` function clears the current figure window by default at each `@manipulate` iteration. If you're manipulating multiple subplots in one figure they will not be displayed correctly. To prevent subplots being destroyed use `withfig(f,clear=false)`. Setting `clear=false` leaves the responsibility for clearing the figure window up to the user. In the case of multiple subplots you can clear each axes object individually, rather than the figure itself. This is shown in the example below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Float64}(Signal{Float64}(2.0, nactions=1),\"α\",2.0,\"2.0\",Interact.OptionDict(DataStructures.OrderedDict(\"1.0\"=>1.0,\"1.1\"=>1.1,\"1.2\"=>1.2,\"1.3\"=>1.3,\"1.4\"=>1.4,\"1.5\"=>1.5,\"1.6\"=>1.6,\"1.7\"=>1.7,\"1.8\"=>1.8,\"1.9\"=>1.9…),Dict(1.2=>\"1.2\",2.8=>\"2.8\",2.0=>\"2.0\",1.5=>\"1.5\",2.2=>\"2.2\",2.1=>\"2.1\",1.4=>\"1.4\",2.6=>\"2.6\",2.3=>\"2.3\",2.9=>\"2.9\"…)),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Float64}(Signal{Float64}(2.0, nactions=1),\"β\",2.0,\"2.0\",Interact.OptionDict(DataStructures.OrderedDict(\"1.0\"=>1.0,\"1.1\"=>1.1,\"1.2\"=>1.2,\"1.3\"=>1.3,\"1.4\"=>1.4,\"1.5\"=>1.5,\"1.6\"=>1.6,\"1.7\"=>1.7,\"1.8\"=>1.8,\"1.9\"=>1.9…),Dict(1.2=>\"1.2\",2.8=>\"2.8\",2.0=>\"2.0\",1.5=>\"1.5\",2.2=>\"2.2\",2.1=>\"2.1\",1.4=>\"1.4\",2.6=>\"2.6\",2.3=>\"2.3\",2.9=>\"2.9\"…)),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Options{:SelectionSlider,Float64}(Signal{Float64}(2.0, nactions=1),\"γ\",2.0,\"2.0\",Interact.OptionDict(DataStructures.OrderedDict(\"1.0\"=>1.0,\"1.1\"=>1.1,\"1.2\"=>1.2,\"1.3\"=>1.3,\"1.4\"=>1.4,\"1.5\"=>1.5,\"1.6\"=>1.6,\"1.7\"=>1.7,\"1.8\"=>1.8,\"1.9\"=>1.9…),Dict(1.2=>\"1.2\",2.8=>\"2.8\",2.0=>\"2.0\",1.5=>\"1.5\",2.2=>\"2.2\",2.1=>\"2.1\",1.4=>\"1.4\",2.6=>\"2.6\",2.3=>\"2.3\",2.9=>\"2.9\"…)),Any[],Any[],true,\"horizontal\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Textbox{AbstractString}(Signal{AbstractString}(a funny plot, nactions=1),\"leg1\",nothing,\"a funny plot\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "Interact.Textbox{AbstractString}(Signal{AbstractString}( an even funnier plot, nactions=1),\"leg2\",nothing,\" an even funnier plot\")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x323b5e1d0>)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "comm_id": "52d01298-542c-44db-a66c-405ff39b1a5a",
      "reactive": true
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f2,axes = subplots(2,1)\n",
    "x = linspace(0,2π,1000)\n",
    "@manipulate for α=1:0.1:3, β=1:0.1:3, γ=1:0.1:3, leg1=\"a funny plot\", leg2=\" an even funnier plot\" \n",
    "    withfig(f2,clear=false) do\n",
    "        for ax in axes\n",
    "            ax[:cla]()\n",
    "        end\n",
    "        axes[1][:plot](x, sin(α*x + cos(β*x + γ)))\n",
    "        axes[2][:plot](x, cos(α*x + sin(β*x + γ)))\n",
    "        axes[1][:legend]([leg1])\n",
    "        axes[2][:legend]([leg2])\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.5.0",
   "language": "julia",
   "name": "julia-0.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.5.0"
  },
  "widgets": {
   "state": {
    "0e44a6fc-6153-4f4d-a0d4-064a007200c0": {
     "views": [
      {
       "cell_index": 13
      }
     ]
    },
    "19a5bac2-e168-47f1-97af-2cd276aaa374": {
     "views": [
      {
       "cell_index": 13
      }
     ]
    },
    "302cba3e-788e-4927-b36a-bb1671cd560e": {
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      {
       "cell_index": 17
      }
     ]
    },
    "38dc67d6-bb6b-4d11-9b7a-901bf76030ab": {
     "views": [
      {
       "cell_index": 9
      }
     ]
    },
    "50ff121b-2410-4f48-96ff-0a3298eaec8e": {
     "views": [
      {
       "cell_index": 8
      }
     ]
    },
    "65925363-81bb-4d7c-99d5-1cc768888f13": {
     "views": [
      {
       "cell_index": 5
      }
     ]
    },
    "66299b25-adad-477a-b33b-ef94e890a60b": {
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